JAACAP: Association of parent-rated sleep disturbances with ADHD symptoms, nine-year follow-up of a population-based cohort study

1 Description of the sample

1.1 Demographics

table1::table1(~ AGE_w1_label + AGE_w2_label + AGE_w3_label +
                 Sex_label + 
                 PARENT_EDUC_label + 
                 MARITAL_STATUS_PARENT_w1_label +
                 REPEAT_label + 
                 NUMBER_SIBLINGS_w1_label | CCA_label,
                 render.missing = NULL, 
                 data = df.table1, overall = "Overall")

	Complete case (N=256)	Missing data (N=799)	Overall (N=1055)
Mean age at wave 1
Mean (SD)	9.18 (1.83)	9.43 (1.78)	9.37 (1.80)
Median [Min, Max]	9.00 [6.00, 12.0]	10.0 [6.00, 12.0]	9.00 [6.00, 12.0]
Mean age at wave 2
Mean (SD)	13.2 (1.83)	13.4 (1.78)	13.4 (1.80)
Median [Min, Max]	13.0 [10.0, 16.0]	14.0 [10.0, 16.0]	13.0 [10.0, 16.0]
Mean age at wave 3
Mean (SD)	18.1 (1.85)	18.2 (1.77)	18.2 (1.81)
Median [Min, Max]	18.0 [15.0, 21.0]	18.0 [15.0, 21.0]	18.0 [15.0, 21.0]
Sex
Male	134 (52.3%)	411 (51.4%)	545 (51.7%)
Female	122 (47.7%)	388 (48.6%)	510 (48.3%)
Parental education
Elementary or secondary school	47 (18.4%)	194 (24.3%)	241 (22.8%)
High school	60 (23.4%)	191 (23.9%)	251 (23.8%)
College or university	149 (58.2%)	413 (51.7%)	562 (53.3%)
Parental marital status at wave 1
Marital life or married	224 (87.5%)	627 (78.5%)	851 (80.7%)
Single	7 (2.7%)	46 (5.8%)	53 (5.0%)
Divorced, separed or widowed	25 (9.8%)	83 (10.4%)	108 (10.2%)
Has ever repeated a grade
No	193 (75.4%)	649 (81.2%)	842 (79.8%)
Yes	62 (24.2%)	149 (18.6%)	211 (20.0%)
Mean number of siblings at wave 1
Mean (SD)	2.25 (0.773)	2.24 (0.843)	2.24 (0.826)
Median [Min, Max]	2.00 [1.00, 5.00]	2.00 [1.00, 7.00]	2.00 [1.00, 7.00]

---

1.2 ADHD and sleep variables

table1::table1(~ ADHD_w1 + ADHD_w2 + ADHD_w3 +
                 ADHD_IN_w1 + ADHD_IN_w2 + ADHD_IN_w3 +
                 ADHD_HY_w1 + ADHD_HY_w2 + ADHD_HY_w3 +
                 SLEEP_w1 + SLEEP_w2 + SLEEP_w3 + 
                 ADHD_DRUG_w1_label + ADHD_DRUG_w2_label + ADHD_DRUG_w3_label + 
                 SLEEP_TRT_w1_label + SLEEP_TRT_w2_label + SLEEP_TRT_w3_label | CCA_label, 
                 render.missing = NULL,
                 data = df.table1, overall = "Overall")

	Complete case (N=256)	Missing data (N=799)	Overall (N=1055)
ADHD_w1
Mean (SD)	1.91 (5.14)	1.92 (4.50)	1.91 (4.67)
Median [Min, Max]	0 [0, 30.0]	0 [0, 32.0]	0 [0, 32.0]
ADHD_w2
Mean (SD)	3.07 (5.62)	2.82 (5.39)	2.89 (5.45)
Median [Min, Max]	0 [0, 32.0]	0 [0, 36.0]	0 [0, 36.0]
ADHD_w3
Mean (SD)	2.54 (4.66)	2.07 (3.87)	2.30 (4.27)
Median [Min, Max]	0 [0, 31.8]	0 [0, 24.0]	0 [0, 31.8]
ADHD_IN_w1
Mean (SD)	1.05 (2.98)	1.00 (2.60)	1.02 (2.70)
Median [Min, Max]	0 [0, 18.0]	0 [0, 18.0]	0 [0, 18.0]
ADHD_IN_w2
Mean (SD)	1.97 (3.76)	1.74 (3.39)	1.80 (3.49)
Median [Min, Max]	0 [0, 18.0]	0 [0, 18.0]	0 [0, 18.0]
ADHD_IN_w3
Mean (SD)	1.47 (3.23)	1.41 (3.09)	1.43 (3.16)
Median [Min, Max]	0 [0, 16.0]	0 [0, 18.0]	0 [0, 18.0]
ADHD_HY_w1
Mean (SD)	0.859 (2.59)	0.912 (2.52)	0.898 (2.54)
Median [Min, Max]	0 [0, 18.0]	0 [0, 16.0]	0 [0, 18.0]
ADHD_HY_w2
Mean (SD)	1.09 (2.48)	1.08 (2.61)	1.09 (2.57)
Median [Min, Max]	0 [0, 14.0]	0 [0, 18.0]	0 [0, 18.0]
ADHD_HY_w3
Mean (SD)	1.07 (2.27)	0.668 (1.53)	0.860 (1.93)
Median [Min, Max]	0 [0, 18.0]	0 [0, 8.00]	0 [0, 18.0]
SLEEP_w1
Mean (SD)	0.821 (0.903)	0.932 (0.928)	0.879 (0.917)
Median [Min, Max]	1.00 [0, 5.50]	1.00 [0, 5.50]	1.00 [0, 5.50]
SLEEP_w2
Mean (SD)	0.915 (1.03)	0.831 (0.968)	0.854 (0.985)
Median [Min, Max]	1.00 [0, 5.00]	0.500 [0, 5.50]	0.571 [0, 5.50]
SLEEP_w3
Mean (SD)	1.20 (1.18)	0.979 (1.11)	1.08 (1.15)
Median [Min, Max]	1.00 [0, 6.50]	1.00 [0, 5.50]	1.00 [0, 6.50]
ADHD_DRUG_w1_label
Medication	0 (0%)	3 (0.4%)	3 (0.3%)
No medication	256 (100%)	572 (71.6%)	828 (78.5%)
ADHD_DRUG_w2_label
Medication	1 (0.4%)	6 (0.8%)	7 (0.7%)
No medication	255 (99.6%)	650 (81.4%)	905 (85.8%)
ADHD_DRUG_w3_label
Medication	0 (0%)	0 (0%)	0 (0%)
No medication	256 (100%)	279 (34.9%)	535 (50.7%)
SLEEP_TRT_w1_label
Treatment	18 (7.0%)	15 (1.9%)	33 (3.1%)
No treatment	238 (93.0%)	265 (33.2%)	503 (47.7%)
SLEEP_TRT_w2_label
Treatment	23 (9.0%)	34 (4.3%)	57 (5.4%)
No treatment	228 (89.1%)	622 (77.8%)	850 (80.6%)
SLEEP_TRT_w3_label
Treatment	17 (6.6%)	16 (2.0%)	33 (3.1%)
No treatment	239 (93.4%)	263 (32.9%)	502 (47.6%)

---

1.3 Pattern of missing data

Graphical representation

df.aux <- df[, c('ADHD_w1', 'ADHD_w2', 'ADHD_w3',
                  'SLEEP_w1', 'SLEEP_w2', 'SLEEP_w3',
                  'PARENT_EDUC',
                  'NUMBER_SIBLINGS_w1',
                  'AGE_w1', 
                  'SEX',
                  'MARITAL_STATUS_PARENT_w1',
                  'REPEAT')]

names(df.aux)[names(df.aux) == "MARITAL_STATUS_PARENT_w1"] <- "MARITAL"
names(df.aux)[names(df.aux) == "NUMBER_SIBLINGS_w1"] <- "N_siblings"
names(df.aux)[names(df.aux) == "PARENT_EDUC"] <- "PAR_EDUC"

VIM::aggr(df.aux, col=c('#66D490','#D56956'),
          sortVars = TRUE, numbers = FALSE,
          sortComsb = TRUE,
          labels = names(df.aux), cex.axis = .7, gap = 1,  
          ylab = c("Histogram of missing data","Pattern"))

## 
##  Variables sorted by number of missings: 
##    Variable        Count
##     ADHD_w3 0.4928909953
##    SLEEP_w3 0.4928909953
##    SLEEP_w1 0.4919431280
##     ADHD_w2 0.1308056872
##    SLEEP_w2 0.1298578199
##     ADHD_w1 0.0407582938
##  N_siblings 0.0407582938
##      AGE_w1 0.0407582938
##     MARITAL 0.0407582938
##      REPEAT 0.0018957346
##    PAR_EDUC 0.0009478673
##         SEX 0.0000000000

Assess whether each variable predicts missingness

pred <- c("AGE_w1", "AGE_w2", "AGE_w3", 
          "ADHD_w2", "ADHD_w3", "SLEEP_w1", "SLEEP_w2", "SLEEP_w3", 
          "SEX", "PARENT_EDUC", "REPEAT", "NUMBER_SIBLINGS_w1",
          "MARITAL_STATUS_PARENT_w1")

GLM_miss <- function(x, dv) {
      formula <- as.formula(paste("CCA", x, sep = " ~ "))
      if (x != "MARITAL_STATUS_PARENT_w1") {
        return(data.frame(
          broom::tidy(glm(formula, data = df, family = binomial)))[2, c(1,2,5)])
      } else {
        return(data.frame(
          broom::tidy(glm(formula, data = df, family = binomial)))[2:3, c(1,2,5)])
      }
}
 
miss <- do.call(rbind, lapply(pred, GLM_miss, dv = "CCA"))

miss[miss$p.value<.05, ]

##                          term   estimate    p.value
## 27                   SLEEP_w3  0.1693296 0.02730126
## 29              PARENT_EDUC.L  0.2815765 0.03472292
## 212 MARITAL_STATUS_PARENT_w12 -0.8534308 0.03883817

---

2 Distribution of the variables

2.1 SLEEP symptoms

ggplot(df_plot_SLEEP, aes(x = sleepsymptoms, fill = waves)) + 
  geom_histogram(alpha = 0.5, position = position_dodge2()) + theme_bw() + 
  ylab("N") + xlab("SLEEP symptoms") +
  guides(fill=guide_legend("Wave")) +
  theme(
    axis.title.y = element_text(size = 11, hjust = 0.5, face="bold"),
    axis.title.x = element_text(face = "bold", size = 11, hjust = 0.5),
    legend.position="top",
    legend.title = element_text(colour = "black", size= 10, face="bold")) +
  coord_cartesian(xlim = c(0, 6))

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

---

2.2 ADHD symptoms

ggplot(df_plot_ADHD, aes(x = adhdsymptoms, fill = waves)) + 
  geom_histogram(alpha = 0.5, position = position_dodge2()) +  theme_bw() + 
  ylab("N") + xlab("ADHD symptoms") +
  guides(fill=guide_legend("Wave")) +
  theme(
    axis.title.y = element_text(size = 11, hjust = 0.5, face="bold"),
    axis.title.x = element_text(face="bold", size = 11, hjust = 0.5),
    legend.position="top",
    legend.title = element_text(colour="black", size=10, face="bold")) +
    coord_cartesian(xlim = c(0, 36))

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

---

3 Preliminary analyses

3.1 Heatmap

heatmaply::heatmaply_cor(
  cor(df[, c('ADHD_w1', 'ADHD_w2', 'ADHD_w3',
               'SLEEP_w1', 'SLEEP_w2', 'SLEEP_w3')],
      method = "spearman",
      use = "complete.obs"),
  xlab = "", 
  ylab = "",
  k_col = 6, 
  k_row = 6)

## Registered S3 methods overwritten by 'registry':
##   method               from 
##   print.registry_field proxy
##   print.registry_entry proxy

---

3.2 Correlation matrix

Correlation coefficients

round(RcmdrMisc::rcorr.adjust(as.matrix(df[, c('ADHD_w1', 'ADHD_w2', 'ADHD_w3',
                                             'SLEEP_w1', 'SLEEP_w2', 'SLEEP_w3')]),
                        type = "spearman",
                        use = "complete.obs")$R[[1]], 3)

##          ADHD_w1 ADHD_w2 ADHD_w3 SLEEP_w1 SLEEP_w2 SLEEP_w3
## ADHD_w1    1.000   0.393   0.297    0.148    0.038    0.085
## ADHD_w2    0.393   1.000   0.381    0.098    0.221    0.117
## ADHD_w3    0.297   0.381   1.000    0.141    0.221    0.279
## SLEEP_w1   0.148   0.098   0.141    1.000    0.335    0.248
## SLEEP_w2   0.038   0.221   0.221    0.335    1.000    0.259
## SLEEP_w3   0.085   0.117   0.279    0.248    0.259    1.000

Associated p-values

round(RcmdrMisc::rcorr.adjust(as.matrix(df[, c('ADHD_w1', 'ADHD_w2', 'ADHD_w3',
                                             'SLEEP_w1', 'SLEEP_w2', 'SLEEP_w3')]),
                        type = "spearman",
                        use = "complete.obs")$R[[3]], 3)

##          ADHD_w1 ADHD_w2 ADHD_w3 SLEEP_w1 SLEEP_w2 SLEEP_w3
## ADHD_w1       NA   0.000   0.000    0.018    0.543    0.176
## ADHD_w2    0.000      NA   0.000    0.119    0.000    0.062
## ADHD_w3    0.000   0.000      NA    0.024    0.000    0.000
## SLEEP_w1   0.018   0.119   0.024       NA    0.000    0.000
## SLEEP_w2   0.543   0.000   0.000    0.000       NA    0.000
## SLEEP_w3   0.176   0.062   0.000    0.000    0.000       NA

---

4 Primary analysis

4.1 Model

RICLPM_PRIM <- '
  SLEEPx =~ 1*SLEEP_w1 + 1*SLEEP_w2 + 1*SLEEP_w3
  ADHDy =~ 1*ADHD_w1 + 1*ADHD_w2 + 1*ADHD_w3
  
  wSLEEP_w1 =~ 1*SLEEP_w1
  wSLEEP_w2 =~ 1*SLEEP_w2
  wSLEEP_w3 =~ 1*SLEEP_w3
  wADHD_w1 =~ 1*ADHD_w1
  wADHD_w2 =~ 1*ADHD_w2
  wADHD_w3 =~ 1*ADHD_w3

  wSLEEP_w2 + wADHD_w2 ~ wSLEEP_w1 + wADHD_w1
  wSLEEP_w3 + wADHD_w3 ~ wSLEEP_w2 + wADHD_w2

  
  wSLEEP_w1 ~~ wADHD_w1 
  wSLEEP_w2 ~~ wADHD_w2
  wSLEEP_w3 ~~ wADHD_w3

  SLEEPx ~~ SLEEPx
  ADHDy ~~ ADHDy
  SLEEPx ~~ ADHDy

  wSLEEP_w1 ~~ wSLEEP_w1 
  wADHD_w1 ~~ wADHD_w1 
  wSLEEP_w2 ~~ wSLEEP_w2
  wADHD_w2 ~~ wADHD_w2 
  wSLEEP_w3 ~~ wSLEEP_w3 
  wADHD_w3 ~~ wADHD_w3 
'

---

4.2 Complete case analysis

4.2.1 RI-CLPM output

RICLPM_CCA <- lavaan::lavaan(RICLPM_PRIM, 
                          data = df,
                          estimator = "WLSMV",
                          meanstructure = TRUE, 
                          int.ov.free = TRUE)

lavaan::summary(RICLPM_CCA, standardized = TRUE, fit.measures = TRUE, ci = TRUE)

## lavaan 0.6-9 ended normally after 172 iterations
## 
##   Estimator                                       DWLS
##   Optimization method                           NLMINB
##   Number of model parameters                        26
##                                                       
##                                                   Used       Total
##   Number of observations                           256        1055
##                                                                   
## Model Test User Model:
##                                               Standard      Robust
##   Test Statistic                                 0.349       0.820
##   Degrees of freedom                                 1           1
##   P-value (Chi-square)                           0.555       0.365
##   Scaling correction factor                                  0.425
##   Shift parameter                                            0.000
##        simple second-order correction                             
## 
## Model Test Baseline Model:
## 
##   Test statistic                               122.611      82.867
##   Degrees of freedom                                15          15
##   P-value                                        0.000       0.000
##   Scaling correction factor                                  1.586
## 
## User Model versus Baseline Model:
## 
##   Comparative Fit Index (CFI)                    1.000       1.000
##   Tucker-Lewis Index (TLI)                       1.091       1.040
##                                                                   
##   Robust Comparative Fit Index (CFI)                            NA
##   Robust Tucker-Lewis Index (TLI)                               NA
## 
## Root Mean Square Error of Approximation:
## 
##   RMSEA                                          0.000       0.000
##   90 Percent confidence interval - lower         0.000       0.000
##   90 Percent confidence interval - upper         0.138       0.160
##   P-value RMSEA <= 0.05                          0.665       0.502
##                                                                   
##   Robust RMSEA                                                  NA
##   90 Percent confidence interval - lower                     0.000
##   90 Percent confidence interval - upper                        NA
## 
## Standardized Root Mean Square Residual:
## 
##   SRMR                                           0.007       0.007
## 
## Parameter Estimates:
## 
##   Standard errors                           Robust.sem
##   Information                                 Expected
##   Information saturated (h1) model        Unstructured
## 
## Latent Variables:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   SLEEPx =~                                                             
##     SLEEP_w1          1.000                               1.000    1.000
##     SLEEP_w2          1.000                               1.000    1.000
##     SLEEP_w3          1.000                               1.000    1.000
##   ADHDy =~                                                              
##     ADHD_w1           1.000                               1.000    1.000
##     ADHD_w2           1.000                               1.000    1.000
##     ADHD_w3           1.000                               1.000    1.000
##   wSLEEP_w1 =~                                                          
##     SLEEP_w1          1.000                               1.000    1.000
##   wSLEEP_w2 =~                                                          
##     SLEEP_w2          1.000                               1.000    1.000
##   wSLEEP_w3 =~                                                          
##     SLEEP_w3          1.000                               1.000    1.000
##   wADHD_w1 =~                                                           
##     ADHD_w1           1.000                               1.000    1.000
##   wADHD_w2 =~                                                           
##     ADHD_w2           1.000                               1.000    1.000
##   wADHD_w3 =~                                                           
##     ADHD_w3           1.000                               1.000    1.000
##    Std.lv  Std.all
##                   
##     0.455    0.504
##     0.455    0.444
##     0.455    0.387
##                   
##     2.551    0.496
##     2.551    0.455
##     2.551    0.548
##                   
##     0.780    0.864
##                   
##     0.918    0.896
##                   
##     1.085    0.922
##                   
##     4.464    0.868
##                   
##     4.998    0.891
##                   
##     3.896    0.837
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   wSLEEP_w2 ~                                                           
##     wSLEEP_w1         0.279    0.150    1.851    0.064   -0.016    0.574
##     wADHD_w1         -0.008    0.017   -0.491    0.624   -0.041    0.025
##   wADHD_w2 ~                                                            
##     wSLEEP_w1         0.278    0.585    0.475    0.635   -0.869    1.425
##     wADHD_w1          0.508    0.106    4.782    0.000    0.300    0.716
##   wSLEEP_w3 ~                                                           
##     wSLEEP_w2         0.208    0.120    1.732    0.083   -0.027    0.443
##     wADHD_w2          0.004    0.021    0.182    0.856   -0.037    0.044
##   wADHD_w3 ~                                                            
##     wSLEEP_w2         0.654    0.327    2.002    0.045    0.014    1.294
##     wADHD_w2          0.216    0.110    1.965    0.049    0.001    0.431
##    Std.lv  Std.all
##                   
##     0.237    0.237
##    -0.040   -0.040
##                   
##     0.043    0.043
##     0.454    0.454
##                   
##     0.176    0.176
##     0.017    0.017
##                   
##     0.154    0.154
##     0.277    0.277
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   wSLEEP_w1 ~~                                                          
##     wADHD_w1          0.857    0.565    1.518    0.129   -0.250    1.964
##  .wSLEEP_w2 ~~                                                          
##    .wADHD_w2          0.960    0.303    3.167    0.002    0.366    1.554
##  .wSLEEP_w3 ~~                                                          
##    .wADHD_w3          1.400    0.575    2.436    0.015    0.274    2.526
##   SLEEPx ~~                                                             
##     ADHDy             0.093    0.264    0.352    0.725   -0.425    0.611
##    Std.lv  Std.all
##                   
##     0.246    0.246
##                   
##     0.243    0.243
##                   
##     0.359    0.359
##                   
##     0.080    0.080
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .SLEEP_w1          0.821    0.056   14.546    0.000    0.710    0.931
##    .SLEEP_w2          0.915    0.064   14.262    0.000    0.789    1.041
##    .SLEEP_w3          1.200    0.074   16.315    0.000    1.056    1.344
##    .ADHD_w1           1.907    0.321    5.934    0.000    1.277    2.537
##    .ADHD_w2           3.069    0.351    8.736    0.000    2.380    3.758
##    .ADHD_w3           2.539    0.291    8.724    0.000    1.969    3.110
##     SLEEPx            0.000                               0.000    0.000
##     ADHDy             0.000                               0.000    0.000
##     wSLEEP_w1         0.000                               0.000    0.000
##    .wSLEEP_w2         0.000                               0.000    0.000
##    .wSLEEP_w3         0.000                               0.000    0.000
##     wADHD_w1          0.000                               0.000    0.000
##    .wADHD_w2          0.000                               0.000    0.000
##    .wADHD_w3          0.000                               0.000    0.000
##    Std.lv  Std.all
##     0.821    0.909
##     0.915    0.893
##     1.200    1.020
##     1.907    0.371
##     3.069    0.547
##     2.539    0.545
##     0.000    0.000
##     0.000    0.000
##     0.000    0.000
##     0.000    0.000
##     0.000    0.000
##     0.000    0.000
##     0.000    0.000
##     0.000    0.000
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##     SLEEPx            0.207    0.089    2.332    0.020    0.033    0.381
##     ADHDy             6.509    3.575    1.821    0.069   -0.498   13.515
##     wSLEEP_w1         0.608    0.152    3.997    0.000    0.310    0.906
##     wADHD_w1         19.927    4.967    4.012    0.000   10.192   29.662
##    .wSLEEP_w2         0.798    0.118    6.751    0.000    0.566    1.030
##    .wADHD_w2         19.548    3.842    5.088    0.000   12.017   27.079
##    .wSLEEP_w3         1.140    0.150    7.614    0.000    0.846    1.433
##    .wADHD_w3         13.360    4.859    2.750    0.006    3.837   22.882
##    .SLEEP_w1          0.000                               0.000    0.000
##    .SLEEP_w2          0.000                               0.000    0.000
##    .SLEEP_w3          0.000                               0.000    0.000
##    .ADHD_w1           0.000                               0.000    0.000
##    .ADHD_w2           0.000                               0.000    0.000
##    .ADHD_w3           0.000                               0.000    0.000
##    Std.lv  Std.all
##     1.000    1.000
##     1.000    1.000
##     1.000    1.000
##     1.000    1.000
##     0.947    0.947
##     0.782    0.782
##     0.967    0.967
##     0.880    0.880
##     0.000    0.000
##     0.000    0.000
##     0.000    0.000
##     0.000    0.000
##     0.000    0.000
##     0.000    0.000

4.2.2 % of variance explained

lavInspect(RICLPM_CCA, "r2")

## wSLEEP_w2  wADHD_w2 wSLEEP_w3  wADHD_w3  SLEEP_w1  SLEEP_w2  SLEEP_w3   ADHD_w1 
##     0.053     0.218     0.033     0.120     1.000     1.000     1.000     1.000 
##   ADHD_w2   ADHD_w3 
##     1.000     1.000

---

5 Additional analyses

5.1 Models

Inattentive symptoms

RICLPM_IN<- '
  SLEEPx =~ 1*SLEEP_w1 + 1*SLEEP_w2 + 1*SLEEP_w3
  ADHDy =~ 1*ADHD_IN_w1 + 1*ADHD_IN_w2 + 1*ADHD_IN_w3
  
  wSLEEP_w1 =~ 1*SLEEP_w1
  wSLEEP_w2 =~ 1*SLEEP_w2
  wSLEEP_w3 =~ 1*SLEEP_w3
  wADHD_IN_w1 =~ 1*ADHD_IN_w1
  wADHD_IN_w2 =~ 1*ADHD_IN_w2
  wADHD_IN_w3 =~ 1*ADHD_IN_w3

  wSLEEP_w2 + wADHD_IN_w2 ~ wSLEEP_w1 + wADHD_IN_w1
  wSLEEP_w3 + wADHD_IN_w3 ~ wSLEEP_w2 + wADHD_IN_w2

  wSLEEP_w1 ~~ wADHD_IN_w1 
  wSLEEP_w2 ~~ wADHD_IN_w2
  wSLEEP_w3 ~~ wADHD_IN_w3

  SLEEPx ~~ SLEEPx
  ADHDy ~~ ADHDy
  SLEEPx ~~ ADHDy

  wSLEEP_w1 ~~ wSLEEP_w1 
  wADHD_IN_w1 ~~ wADHD_IN_w1 
  wSLEEP_w2 ~~ wSLEEP_w2
  wADHD_IN_w2 ~~ wADHD_IN_w2 
  wSLEEP_w3 ~~ wSLEEP_w3 
  wADHD_IN_w3 ~~ wADHD_IN_w3 
'

Hyperactive-impulsive symptoms

RICLPM_HY<- '
  SLEEPx =~ 1*SLEEP_w1 + 1*SLEEP_w2 + 1*SLEEP_w3
  ADHDy =~ 1*ADHD_HY_w1 + 1*ADHD_HY_w2 + 1*ADHD_HY_w3
  
  wSLEEP_w1 =~ 1*SLEEP_w1
  wSLEEP_w2 =~ 1*SLEEP_w2
  wSLEEP_w3 =~ 1*SLEEP_w3
  wADHD_HY_w1 =~ 1*ADHD_HY_w1
  wADHD_HY_w2 =~ 1*ADHD_HY_w2
  wADHD_HY_w3 =~ 1*ADHD_HY_w3

  wSLEEP_w2 + wADHD_HY_w2 ~ wSLEEP_w1 + wADHD_HY_w1
  wSLEEP_w3 + wADHD_HY_w3 ~ wSLEEP_w2 + wADHD_HY_w2

  wSLEEP_w1 ~~ wADHD_HY_w1 
  wSLEEP_w2 ~~ wADHD_HY_w2
  wSLEEP_w3 ~~ wADHD_HY_w3

  SLEEPx ~~ SLEEPx
  ADHDy ~~ ADHDy
  SLEEPx ~~ ADHDy

  wSLEEP_w1 ~~ wSLEEP_w1 
  wADHD_HY_w1 ~~ wADHD_HY_w1 
  wSLEEP_w2 ~~ wSLEEP_w2
  wADHD_HY_w2 ~~ wADHD_HY_w2 
  wSLEEP_w3 ~~ wSLEEP_w3 
  wADHD_HY_w3 ~~ wADHD_HY_w3 
'

---

5.2 Inattentive

5.2.1 RI-CLPM output

RICLPM_IN_FIT <- lavaan::lavaan(RICLPM_IN, 
                          data = df,
                          estimator = "WLSMV",
                          meanstructure = TRUE, 
                          int.ov.free = TRUE)

lavaan::summary(RICLPM_IN_FIT, standardized = TRUE, fit.measures = TRUE, ci = TRUE)

## lavaan 0.6-9 ended normally after 147 iterations
## 
##   Estimator                                       DWLS
##   Optimization method                           NLMINB
##   Number of model parameters                        26
##                                                       
##                                                   Used       Total
##   Number of observations                           256        1055
##                                                                   
## Model Test User Model:
##                                               Standard      Robust
##   Test Statistic                                 0.240       0.573
##   Degrees of freedom                                 1           1
##   P-value (Chi-square)                           0.624       0.449
##   Scaling correction factor                                  0.420
##   Shift parameter                                            0.000
##        simple second-order correction                             
## 
## Model Test Baseline Model:
## 
##   Test statistic                               121.256      80.464
##   Degrees of freedom                                15          15
##   P-value                                        0.000       0.000
##   Scaling correction factor                                  1.623
## 
## User Model versus Baseline Model:
## 
##   Comparative Fit Index (CFI)                    1.000       1.000
##   Tucker-Lewis Index (TLI)                       1.107       1.098
##                                                                   
##   Robust Comparative Fit Index (CFI)                            NA
##   Robust Tucker-Lewis Index (TLI)                               NA
## 
## Root Mean Square Error of Approximation:
## 
##   RMSEA                                          0.000       0.000
##   90 Percent confidence interval - lower         0.000       0.000
##   90 Percent confidence interval - upper         0.131       0.150
##   P-value RMSEA <= 0.05                          0.720       0.577
##                                                                   
##   Robust RMSEA                                                  NA
##   90 Percent confidence interval - lower                     0.000
##   90 Percent confidence interval - upper                        NA
## 
## Standardized Root Mean Square Residual:
## 
##   SRMR                                           0.007       0.007
## 
## Parameter Estimates:
## 
##   Standard errors                           Robust.sem
##   Information                                 Expected
##   Information saturated (h1) model        Unstructured
## 
## Latent Variables:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   SLEEPx =~                                                             
##     SLEEP_w1          1.000                               1.000    1.000
##     SLEEP_w2          1.000                               1.000    1.000
##     SLEEP_w3          1.000                               1.000    1.000
##   ADHDy =~                                                              
##     ADHD_IN_w1        1.000                               1.000    1.000
##     ADHD_IN_w2        1.000                               1.000    1.000
##     ADHD_IN_w3        1.000                               1.000    1.000
##   wSLEEP_w1 =~                                                          
##     SLEEP_w1          1.000                               1.000    1.000
##   wSLEEP_w2 =~                                                          
##     SLEEP_w2          1.000                               1.000    1.000
##   wSLEEP_w3 =~                                                          
##     SLEEP_w3          1.000                               1.000    1.000
##   wADHD_IN_w1 =~                                                        
##     ADHD_IN_w1        1.000                               1.000    1.000
##   wADHD_IN_w2 =~                                                        
##     ADHD_IN_w2        1.000                               1.000    1.000
##   wADHD_IN_w3 =~                                                        
##     ADHD_IN_w3        1.000                               1.000    1.000
##    Std.lv  Std.all
##                   
##     0.449    0.497
##     0.449    0.438
##     0.449    0.381
##                   
##     1.567    0.527
##     1.567    0.418
##     1.567    0.485
##                   
##     0.783    0.868
##                   
##     0.921    0.899
##                   
##     1.088    0.924
##                   
##     2.529    0.850
##                   
##     3.405    0.908
##                   
##     2.829    0.875
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   wSLEEP_w2 ~                                                           
##     wSLEEP_w1         0.274    0.154    1.774    0.076   -0.029    0.576
##     wADHD_IN_w1       0.003    0.034    0.082    0.934   -0.065    0.070
##   wADHD_IN_w2 ~                                                         
##     wSLEEP_w1         0.275    0.376    0.731    0.465   -0.463    1.013
##     wADHD_IN_w1       0.607    0.148    4.103    0.000    0.317    0.897
##   wSLEEP_w3 ~                                                           
##     wSLEEP_w2         0.199    0.120    1.663    0.096   -0.036    0.434
##     wADHD_IN_w2       0.014    0.028    0.508    0.612   -0.041    0.069
##   wADHD_IN_w3 ~                                                         
##     wSLEEP_w2         0.628    0.289    2.173    0.030    0.062    1.194
##     wADHD_IN_w2       0.266    0.110    2.416    0.016    0.050    0.482
##    Std.lv  Std.all
##                   
##     0.233    0.233
##     0.008    0.008
##                   
##     0.063    0.063
##     0.451    0.451
##                   
##     0.169    0.169
##     0.044    0.044
##                   
##     0.205    0.205
##     0.320    0.320
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   wSLEEP_w1 ~~                                                          
##     wADHD_IN_w1       0.662    0.392    1.690    0.091   -0.106    1.431
##  .wSLEEP_w2 ~~                                                          
##    .wADHD_IN_w2       0.619    0.202    3.065    0.002    0.223    1.015
##  .wSLEEP_w3 ~~                                                          
##    .wADHD_IN_w3       1.143    0.388    2.944    0.003    0.382    1.903
##   SLEEPx ~~                                                             
##     ADHDy            -0.068    0.210   -0.323    0.746   -0.479    0.343
##    Std.lv  Std.all
##                   
##     0.334    0.334
##                   
##     0.231    0.231
##                   
##     0.417    0.417
##                   
##    -0.096   -0.096
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .SLEEP_w1          0.821    0.056   14.546    0.000    0.710    0.931
##    .SLEEP_w2          0.915    0.064   14.262    0.000    0.789    1.041
##    .SLEEP_w3          1.200    0.074   16.315    0.000    1.056    1.344
##    .ADHD_IN_w1        1.048    0.186    5.634    0.000    0.683    1.412
##    .ADHD_IN_w2        1.970    0.235    8.389    0.000    1.510    2.430
##    .ADHD_IN_w3        1.466    0.202    7.253    0.000    1.070    1.862
##     SLEEPx            0.000                               0.000    0.000
##     ADHDy             0.000                               0.000    0.000
##     wSLEEP_w1         0.000                               0.000    0.000
##    .wSLEEP_w2         0.000                               0.000    0.000
##    .wSLEEP_w3         0.000                               0.000    0.000
##     wADHD_IN_w1       0.000                               0.000    0.000
##    .wADHD_IN_w2       0.000                               0.000    0.000
##    .wADHD_IN_w3       0.000                               0.000    0.000
##    Std.lv  Std.all
##     0.821    0.909
##     0.915    0.893
##     1.200    1.020
##     1.048    0.352
##     1.970    0.525
##     1.466    0.453
##     0.000    0.000
##     0.000    0.000
##     0.000    0.000
##     0.000    0.000
##     0.000    0.000
##     0.000    0.000
##     0.000    0.000
##     0.000    0.000
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##     SLEEPx            0.202    0.091    2.226    0.026    0.024    0.379
##     ADHDy             2.456    1.586    1.548    0.122   -0.653    5.565
##     wSLEEP_w1         0.613    0.156    3.924    0.000    0.307    0.919
##     wADHD_IN_w1       6.398    2.264    2.825    0.005    1.959   10.836
##    .wSLEEP_w2         0.802    0.118    6.791    0.000    0.570    1.033
##    .wADHD_IN_w2       8.973    1.658    5.413    0.000    5.724   12.223
##    .wSLEEP_w3         1.143    0.150    7.602    0.000    0.848    1.437
##    .wADHD_IN_w3       6.582    1.620    4.062    0.000    3.406    9.758
##    .SLEEP_w1          0.000                               0.000    0.000
##    .SLEEP_w2          0.000                               0.000    0.000
##    .SLEEP_w3          0.000                               0.000    0.000
##    .ADHD_IN_w1        0.000                               0.000    0.000
##    .ADHD_IN_w2        0.000                               0.000    0.000
##    .ADHD_IN_w3        0.000                               0.000    0.000
##    Std.lv  Std.all
##     1.000    1.000
##     1.000    1.000
##     1.000    1.000
##     1.000    1.000
##     0.945    0.945
##     0.774    0.774
##     0.966    0.966
##     0.823    0.823
##     0.000    0.000
##     0.000    0.000
##     0.000    0.000
##     0.000    0.000
##     0.000    0.000
##     0.000    0.000

5.2.2 % of variance explained

lavInspect(RICLPM_IN_FIT, "r2")

##   wSLEEP_w2 wADHD_IN_w2   wSLEEP_w3 wADHD_IN_w3    SLEEP_w1    SLEEP_w2 
##       0.055       0.226       0.034       0.177       1.000       1.000 
##    SLEEP_w3  ADHD_IN_w1  ADHD_IN_w2  ADHD_IN_w3 
##       1.000       1.000       1.000       1.000

---

5.3 Hyperactive

5.3.1 RI-CLPM output

RICLPM_HY_FIT <- lavaan::lavaan(RICLPM_HY, 
                          data = df,
                          estimator = "WLSMV",
                          meanstructure = TRUE, 
                          int.ov.free = TRUE)

lavaan::summary(RICLPM_HY_FIT, standardized = TRUE, fit.measures = TRUE, ci = TRUE)

## lavaan 0.6-9 ended normally after 110 iterations
## 
##   Estimator                                       DWLS
##   Optimization method                           NLMINB
##   Number of model parameters                        26
##                                                       
##                                                   Used       Total
##   Number of observations                           256        1055
##                                                                   
## Model Test User Model:
##                                               Standard      Robust
##   Test Statistic                                 0.229       0.385
##   Degrees of freedom                                 1           1
##   P-value (Chi-square)                           0.632       0.535
##   Scaling correction factor                                  0.595
##   Shift parameter                                            0.000
##        simple second-order correction                             
## 
## Model Test Baseline Model:
## 
##   Test statistic                                85.311      65.681
##   Degrees of freedom                                15          15
##   P-value                                        0.000       0.000
##   Scaling correction factor                                  1.387
## 
## User Model versus Baseline Model:
## 
##   Comparative Fit Index (CFI)                    1.000       1.000
##   Tucker-Lewis Index (TLI)                       1.164       1.182
##                                                                   
##   Robust Comparative Fit Index (CFI)                            NA
##   Robust Tucker-Lewis Index (TLI)                               NA
## 
## Root Mean Square Error of Approximation:
## 
##   RMSEA                                          0.000       0.000
##   90 Percent confidence interval - lower         0.000       0.000
##   90 Percent confidence interval - upper         0.130       0.141
##   P-value RMSEA <= 0.05                          0.726       0.649
##                                                                   
##   Robust RMSEA                                                  NA
##   90 Percent confidence interval - lower                     0.000
##   90 Percent confidence interval - upper                        NA
## 
## Standardized Root Mean Square Residual:
## 
##   SRMR                                           0.006       0.006
## 
## Parameter Estimates:
## 
##   Standard errors                           Robust.sem
##   Information                                 Expected
##   Information saturated (h1) model        Unstructured
## 
## Latent Variables:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   SLEEPx =~                                                             
##     SLEEP_w1          1.000                               1.000    1.000
##     SLEEP_w2          1.000                               1.000    1.000
##     SLEEP_w3          1.000                               1.000    1.000
##   ADHDy =~                                                              
##     ADHD_HY_w1        1.000                               1.000    1.000
##     ADHD_HY_w2        1.000                               1.000    1.000
##     ADHD_HY_w3        1.000                               1.000    1.000
##   wSLEEP_w1 =~                                                          
##     SLEEP_w1          1.000                               1.000    1.000
##   wSLEEP_w2 =~                                                          
##     SLEEP_w2          1.000                               1.000    1.000
##   wSLEEP_w3 =~                                                          
##     SLEEP_w3          1.000                               1.000    1.000
##   wADHD_HY_w1 =~                                                        
##     ADHD_HY_w1        1.000                               1.000    1.000
##   wADHD_HY_w2 =~                                                        
##     ADHD_HY_w2        1.000                               1.000    1.000
##   wADHD_HY_w3 =~                                                        
##     ADHD_HY_w3        1.000                               1.000    1.000
##    Std.lv  Std.all
##                   
##     0.465    0.515
##     0.465    0.453
##     0.465    0.395
##                   
##     1.229    0.475
##     1.229    0.496
##     1.229    0.541
##                   
##     0.774    0.857
##                   
##     0.914    0.891
##                   
##     1.081    0.919
##                   
##     2.279    0.880
##                   
##     2.152    0.868
##                   
##     1.911    0.841
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   wSLEEP_w2 ~                                                           
##     wSLEEP_w1         0.259    0.149    1.737    0.082   -0.033    0.552
##     wADHD_HY_w1      -0.022    0.033   -0.688    0.492   -0.086    0.041
##   wADHD_HY_w2 ~                                                         
##     wSLEEP_w1         0.031    0.402    0.077    0.939   -0.758    0.820
##     wADHD_HY_w1       0.287    0.114    2.523    0.012    0.064    0.510
##   wSLEEP_w3 ~                                                           
##     wSLEEP_w2         0.198    0.118    1.671    0.095   -0.034    0.430
##     wADHD_HY_w2       0.005    0.053    0.104    0.917   -0.098    0.109
##   wADHD_HY_w3 ~                                                         
##     wSLEEP_w2         0.118    0.174    0.677    0.499   -0.224    0.459
##     wADHD_HY_w2       0.049    0.141    0.346    0.730   -0.228    0.326
##    Std.lv  Std.all
##                   
##     0.220    0.220
##    -0.056   -0.056
##                   
##     0.011    0.011
##     0.304    0.304
##                   
##     0.167    0.167
##     0.011    0.011
##                   
##     0.056    0.056
##     0.055    0.055
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   wSLEEP_w1 ~~                                                          
##     wADHD_HY_w1       0.264    0.236    1.119    0.263   -0.199    0.727
##  .wSLEEP_w2 ~~                                                          
##    .wADHD_HY_w2       0.326    0.176    1.851    0.064   -0.019    0.671
##  .wSLEEP_w3 ~~                                                          
##    .wADHD_HY_w3       0.291    0.247    1.179    0.238   -0.193    0.775
##   SLEEPx ~~                                                             
##     ADHDy             0.092    0.114    0.809    0.418   -0.131    0.315
##    Std.lv  Std.all
##                   
##     0.150    0.150
##                   
##     0.178    0.178
##                   
##     0.144    0.144
##                   
##     0.161    0.161
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .SLEEP_w1          0.821    0.056   14.546    0.000    0.710    0.931
##    .SLEEP_w2          0.915    0.064   14.262    0.000    0.789    1.041
##    .SLEEP_w3          1.200    0.074   16.315    0.000    1.056    1.344
##    .ADHD_HY_w1        0.859    0.162    5.308    0.000    0.542    1.176
##    .ADHD_HY_w2        1.092    0.155    7.052    0.000    0.788    1.395
##    .ADHD_HY_w3        1.069    0.142    7.530    0.000    0.791    1.347
##     SLEEPx            0.000                               0.000    0.000
##     ADHDy             0.000                               0.000    0.000
##     wSLEEP_w1         0.000                               0.000    0.000
##    .wSLEEP_w2         0.000                               0.000    0.000
##    .wSLEEP_w3         0.000                               0.000    0.000
##     wADHD_HY_w1       0.000                               0.000    0.000
##    .wADHD_HY_w2       0.000                               0.000    0.000
##    .wADHD_HY_w3       0.000                               0.000    0.000
##    Std.lv  Std.all
##     0.821    0.909
##     0.915    0.892
##     1.200    1.020
##     0.859    0.332
##     1.092    0.441
##     1.069    0.471
##     0.000    0.000
##     0.000    0.000
##     0.000    0.000
##     0.000    0.000
##     0.000    0.000
##     0.000    0.000
##     0.000    0.000
##     0.000    0.000
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##     SLEEPx            0.216    0.085    2.554    0.011    0.050    0.382
##     ADHDy             1.510    1.036    1.457    0.145   -0.522    3.541
##     wSLEEP_w1         0.599    0.148    4.037    0.000    0.308    0.889
##     wADHD_HY_w1       5.194    1.195    4.345    0.000    2.851    7.537
##    .wSLEEP_w2         0.796    0.118    6.752    0.000    0.565    1.027
##    .wADHD_HY_w2       4.196    0.972    4.319    0.000    2.292    6.101
##    .wSLEEP_w3         1.135    0.150    7.586    0.000    0.842    1.429
##    .wADHD_HY_w3       3.624    1.472    2.462    0.014    0.739    6.509
##    .SLEEP_w1          0.000                               0.000    0.000
##    .SLEEP_w2          0.000                               0.000    0.000
##    .SLEEP_w3          0.000                               0.000    0.000
##    .ADHD_HY_w1        0.000                               0.000    0.000
##    .ADHD_HY_w2        0.000                               0.000    0.000
##    .ADHD_HY_w3        0.000                               0.000    0.000
##    Std.lv  Std.all
##     1.000    1.000
##     1.000    1.000
##     1.000    1.000
##     1.000    1.000
##     0.952    0.952
##     0.907    0.907
##     0.971    0.971
##     0.993    0.993
##     0.000    0.000
##     0.000    0.000
##     0.000    0.000
##     0.000    0.000
##     0.000    0.000
##     0.000    0.000

5.3.2 % of variance explained

lavInspect(RICLPM_HY_FIT, "r2")

##   wSLEEP_w2 wADHD_HY_w2   wSLEEP_w3 wADHD_HY_w3    SLEEP_w1    SLEEP_w2 
##       0.048       0.093       0.029       0.007       1.000       1.000 
##    SLEEP_w3  ADHD_HY_w1  ADHD_HY_w2  ADHD_HY_w3 
##       1.000       1.000       1.000       1.000

---

5.4 Multiple imputation

5.4.1 Imputation model

df.mice.aux <- df[, c('ADHD_w1', 'ADHD_w2', 'ADHD_w3',
                  'SLEEP_w1', 'SLEEP_w2', 'SLEEP_w3',
                  'PARENT_EDUC',
                  'NUMBER_SIBLINGS_w1',
                  'AGE_w1',
                  'SEX',
                  'MARITAL_STATUS_PARENT_w1',
                  'REPEAT'
                  )]

df.imput <- mice::mice(df.mice.aux, m = 50)
  
  mice.imp <- NULL
  for(i in 1:df.imput$m) {
    mice.imp[[i]] <- mice::complete(df.imput, action=i)
  }
  RICLPM_PRIM.fit.imput <- runMI(RICLPM_PRIM, 
                                 data = mice.imp,
                                 fun = "lavaan",
                                 estimator = "WLSMV",
                                 meanstructure = TRUE, 
                                 int.ov.free = TRUE)

5.4.2 RI-CLPM output

lavaan::summary(RICLPM_PRIM.fit.imput, ci = TRUE, rsquare = TRUE, 
                test = "D2", pool.robust = TRUE, standardized = TRUE)

## lavaan.mi object based on 50 imputed data sets. 
## See class?lavaan.mi help page for available methods. 
## 
## Convergence information:
## The model converged on 50 imputed data sets 
## 
## Heywood cases detected for data set(s) 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50 
## These are not necessarily a cause for concern, unless a pooled estimate is also a Heywood case. 
## 
## Rubin's (1987) rules were used to pool point and SE estimates across 50 imputed data sets, and to calculate degrees of freedom for each parameter's t test and CI.

## 
## Parameter Estimates:
## 
##   Standard errors                           Robust.sem
##   Information                                 Expected
##   Information saturated (h1) model        Unstructured
## 
## Latent Variables:
##                    Estimate  Std.Err  t-value       df  P(>|t|) ci.lower
##   SLEEPx =~                                                             
##     SLEEP_w1          1.000                                        1.000
##     SLEEP_w2          1.000                                        1.000
##     SLEEP_w3          1.000                                        1.000
##   ADHDy =~                                                              
##     ADHD_w1           1.000                                        1.000
##     ADHD_w2           1.000                                        1.000
##     ADHD_w3           1.000                                        1.000
##   wSLEEP_w1 =~                                                          
##     SLEEP_w1          1.000                                        1.000
##   wSLEEP_w2 =~                                                          
##     SLEEP_w2          1.000                                        1.000
##   wSLEEP_w3 =~                                                          
##     SLEEP_w3          1.000                                        1.000
##   wADHD_w1 =~                                                           
##     ADHD_w1           1.000                                        1.000
##   wADHD_w2 =~                                                           
##     ADHD_w2           1.000                                        1.000
##   wADHD_w3 =~                                                           
##     ADHD_w3           1.000                                        1.000
##  ci.upper   Std.lv  Std.all
##                            
##     1.000    0.465    0.516
##     1.000    0.465    0.473
##     1.000    0.465    0.407
##                            
##     1.000    1.896    0.408
##     1.000    1.896    0.349
##     1.000    1.896    0.441
##                            
##     1.000    0.772    0.857
##                            
##     1.000    0.866    0.881
##                            
##     1.000    1.043    0.913
##                            
##     1.000    4.246    0.913
##                            
##     1.000    5.084    0.937
##                            
##     1.000    3.863    0.898
## 
## Regressions:
##                    Estimate  Std.Err  t-value       df  P(>|t|) ci.lower
##   wSLEEP_w2 ~                                                           
##     wSLEEP_w1         0.141    0.102    1.373  145.399    0.172   -0.062
##     wADHD_w1          0.028    0.014    2.004  284.627    0.046    0.001
##   wADHD_w2 ~                                                            
##     wSLEEP_w1         0.268    0.401    0.670  229.507    0.504   -0.521
##     wADHD_w1          0.474    0.092    5.164  462.049    0.000    0.294
##   wSLEEP_w3 ~                                                           
##     wSLEEP_w2         0.230    0.079    2.912  121.397    0.004    0.074
##     wADHD_w2          0.009    0.012    0.792  192.775    0.429   -0.014
##   wADHD_w3 ~                                                            
##     wSLEEP_w2         0.433    0.239    1.816  206.561    0.071   -0.037
##     wADHD_w2          0.324    0.069    4.721  263.197    0.000    0.189
##  ci.upper   Std.lv  Std.all
##                            
##     0.343    0.126    0.126
##     0.055    0.137    0.137
##                            
##     1.057    0.041    0.041
##     0.655    0.396    0.396
##                            
##     0.386    0.191    0.191
##     0.033    0.046    0.046
##                            
##     0.903    0.097    0.097
##     0.460    0.427    0.427
## 
## Covariances:
##                    Estimate  Std.Err  t-value       df  P(>|t|) ci.lower
##   wSLEEP_w1 ~~                                                          
##     wADHD_w1          0.759    0.302    2.515  225.634    0.013    0.164
##  .wSLEEP_w2 ~~                                                          
##    .wADHD_w2          0.940    0.230    4.096  840.137    0.000    0.490
##  .wSLEEP_w3 ~~                                                          
##    .wADHD_w3          1.072    0.289    3.704  315.041    0.000    0.502
##   SLEEPx ~~                                                             
##     ADHDy             0.036    0.209    0.172  133.537    0.864   -0.377
##  ci.upper   Std.lv  Std.all
##                            
##     1.354    0.232    0.232
##                            
##     1.391    0.239    0.239
##                            
##     1.641    0.307    0.307
##                            
##     0.448    0.041    0.041
## 
## Intercepts:
##                    Estimate  Std.Err  t-value       df  P(>|t|) ci.lower
##    .SLEEP_w1          0.848    0.036   23.449  244.811    0.000    0.777
##    .SLEEP_w2          0.858    0.040   21.654 5205.523    0.000    0.781
##    .SLEEP_w3          1.089    0.046   23.656  247.966    0.000    0.999
##    .ADHD_w1           1.902    0.187   10.183      Inf    0.000    1.536
##    .ADHD_w2           2.897    0.219   13.237 3673.754    0.000    2.468
##    .ADHD_w3           2.343    0.173   13.503  292.915    0.000    2.001
##     SLEEPx            0.000                                        0.000
##     ADHDy             0.000                                        0.000
##     wSLEEP_w1         0.000                                        0.000
##    .wSLEEP_w2         0.000                                        0.000
##    .wSLEEP_w3         0.000                                        0.000
##     wADHD_w1          0.000                                        0.000
##    .wADHD_w2          0.000                                        0.000
##    .wADHD_w3          0.000                                        0.000
##  ci.upper   Std.lv  Std.all
##     0.920    0.848    0.942
##     0.936    0.858    0.874
##     1.180    1.089    0.954
##     2.268    1.902    0.409
##     3.326    2.897    0.534
##     2.684    2.343    0.544
##     0.000    0.000    0.000
##     0.000    0.000    0.000
##     0.000    0.000    0.000
##     0.000    0.000    0.000
##     0.000    0.000    0.000
##     0.000    0.000    0.000
##     0.000    0.000    0.000
##     0.000    0.000    0.000
## 
## Variances:
##                    Estimate  Std.Err  t-value       df  P(>|t|) ci.lower
##     SLEEPx            0.216    0.066    3.252  109.290    0.002    0.084
##     ADHDy             3.594    2.062    1.743  196.396    0.083   -0.473
##     wSLEEP_w1         0.596    0.084    7.073  157.502    0.000    0.429
##     wADHD_w1         18.030    3.139    5.743  574.686    0.000   11.864
##    .wSLEEP_w2         0.717    0.074    9.717  205.634    0.000    0.572
##    .wADHD_w2         21.555    2.912    7.402 3228.245    0.000   15.845
##    .wSLEEP_w3         1.042    0.085   12.293  261.853    0.000    0.875
##    .wADHD_w3         11.704    2.180    5.368  465.790    0.000    7.419
##    .SLEEP_w1          0.000                                        0.000
##    .SLEEP_w2          0.000                                        0.000
##    .SLEEP_w3          0.000                                        0.000
##    .ADHD_w1           0.000                                        0.000
##    .ADHD_w2           0.000                                        0.000
##    .ADHD_w3           0.000                                        0.000
##  ci.upper   Std.lv  Std.all
##     0.348    1.000    1.000
##     7.661    1.000    1.000
##     0.762    1.000    1.000
##    24.197    1.000    1.000
##     0.863    0.958    0.958
##    27.265    0.834    0.834
##     1.208    0.957    0.957
##    15.988    0.784    0.784
##     0.000    0.000    0.000
##     0.000    0.000    0.000
##     0.000    0.000    0.000
##     0.000    0.000    0.000
##     0.000    0.000    0.000
##     0.000    0.000    0.000
## 
## R-Square:
##                    Estimate
##     wSLEEP_w2         0.042
##     wADHD_w2          0.166
##     wSLEEP_w3         0.043
##     wADHD_w3          0.216
##     SLEEP_w1          1.000
##     SLEEP_w2          1.000
##     SLEEP_w3          1.000
##     ADHD_w1           1.000
##     ADHD_w2           1.000
##     ADHD_w3           1.000